@Article{ChenLMBDSSHLO:2018:MaCrCr,
author = "Chen, Yaoliang and Lu, Dengsheng and Moran, Emilio and Batistella,
Mateus and Dutra, Luciano Vieira and Sanches, Ieda Del'Arco and
Silva, Ramon Felipe Bicudo da and Huang, Jingfeng and Luiz,
Alfredo Jos{\'e} Barreto and Oliveira, Maria Antonia Falc{\~a}o
de",
affiliation = "{Zhejiang Agriculture and Forestry University} and {Zhejiang
Agriculture and Forestry University} and {Michigan State
Universit} and {Empresa Brasileira de Pesquisa Agropecu{\'a}ria
(EMBRAPA)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}
and {Instituto Nacional de Pesquisas Espaciais (INPE)} and
{Universidade Estadual de Campinas (UNICAMP)} and {Zhejiang
University} and {Empresa Brasileira de Pesquisa Agropecu{\'a}ria
(EMBRAPA)} and {Instituto Nacional de Pesquisas Espaciais
(INPE)}",
title = "Mapping croplands, cropping patterns, and crop types using MODIS
time-series data",
journal = "International Journal of Applied Earth Observation and
Geoinformation",
year = "2018",
volume = "69",
pages = "133--147",
month = "July",
note = "{Pr{\^e}mio CAPES Elsevier 2023 - ODS 2: Fome zero e Agricultura
sustent{\'a}vel}",
keywords = "Croplands, Cropping patterns, Crop types, MODIS NDVI, Decision
tree classifier, Brazil.",
abstract = "The importance of mapping regional and global cropland
distribution in timely ways has been recognized, but separation of
crop types and multiple cropping patterns is challenging due to
their spectral similarity. This study developed a new approach to
identify crop types (including soy, cotton and maize) and cropping
patterns (Soy Maize, Soy-Cotton, Soy-Pasture, Soy-Fallow,
Fallow-Cotton and Single crop) in the state of Mato Grosso,
Brazil. The Moderate Resolution Imaging Spectroradiometer (MODIS)
normalized difference vegetation index (NDVI) time series data for
2015 and 2016 and field survey data were used in this research.
The major steps of this proposed approach include: (1)
reconstructing NDVI time series data by removing the
cloud-contaminated pixels using the temporal interpolation
algorithm, (2) identifying the best periods and developing
temporal indices and phenological parameters to distinguish
croplands from other land cover types, and (3) developing crop
temporal indices to extract cropping patterns using NDVI
time-series data and group cropping patterns into crop types.
Decision tree classifier was used to map cropping patterns based
on these temporal indices. Croplands from Landsat imagery in 2016,
cropping pattern samples from field survey in 2016, and the
planted area of crop types in 2015 were used for accuracy
assessment. Overall accuracies of approximately 90%, 73% and 86%,
respectively were obtained for croplands, cropping patterns, and
crop types. The adjusted coefficients of determination of total
crop, soy, maize, and cotton areas with corresponding statistical
areas were 0.94, 0.94, 0.88 and 0.88, respectively. This research
indicates that the proposed approach is promising for mapping
large-scale croplands, their cropping patterns and crop types.",
doi = "10.1016/j.jag.2018.03.005",
url = "http://dx.doi.org/10.1016/j.jag.2018.03.005",
issn = "0303-2434",
language = "en",
targetfile = "chen_mapping.pdf",
urlaccessdate = "27 abr. 2024"
}